COVID-19 diagnosis and disease severity prediction assessment through an innovative AI-based model

Abstract

Introduction: CT imaging has been widely used during the COVID-19 pandemic to diagnose and assess disease severity. Its use for diagnosis is not indicated apart from specific settings such as triage of patients for referral to RT-PCR testing or severity assessment. Nowadays, the added value of AI-based models is still unknown and has to be addressed.

Methods: We evaluated the added value of an automated lung involvement assessment tool, named icolung. Since software version 7.0, icolung automatically extracts the Severity Score proposed by Pan F. et al., (2020, Radiology), to help radiologists assess the severity of lung involvement in COVID-19 infected patients. We evaluate retrospectively a group of 785 COVID-19 positive patients compared to a group of 1049 COVID-19 negative patients. We used the severity score (SS) in order to predict the positivity of COVID-19 PCR testing and evaluated the potential impact in the prediction of patients’ outcome.

Results: The icolung SS allows to identify infected (PCR-proven) COVID-19 patients with a sensitivity of 83% and a specificity of 77% (AUC of 0.86, 95% CI 0.85-0.88) for patients with a SS of more than 1.5 on a scale of 0 to 25. An SS of > 7.5 identifies patients at risk of ICU admission with a sensitivity of 70% and specificity of 65% (AUC of 0.74, p<0.0001).

Conclusion: The severity score as estimated via icolung allows to identify positive PCR-tested COVID-19 patients and helps to predict ICU admission.

This automated evaluation tool can support clinicians with the in-hospital management of patients (suspected to be) infected with COVID-19.